
Pileup Mitigation with Machine Learning (PUMML)
Pileup involves the contamination of the energy distribution arising fro...
read it

Growing Artificial Neural Networks
Pruning is a legitimate method for reducing the size of a neural network...
read it

Cluster, Classify, Regress: A General Method For Learning Discountinous Functions
This paper presents a method for solving the supervised learning problem...
read it

From Patterson Maps to Atomic Coordinates: Training a Deep Neural Network to Solve the Phase Problem for a Simplified Case
This work demonstrates that, for a simple case of 10 randomly positioned...
read it

A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network
This work addresses the inverse identification of apparent elastic prope...
read it

Deep EnergyBased NARX Models
This paper is directed towards the problem of learning nonlinear ARX mod...
read it

Exploiting the Logits: Joint Sign Language Recognition and SpellCorrection
Machine learning techniques have excelled in the automatic semantic anal...
read it
Coercing Machine Learning to Output Physically Accurate Results
Many machine/deep learning artificial neural networks are trained to simply be interpolation functions that map input variables to output values interpolated from the training data in a linear/nonlinear fashion. Even when the input/output pairs of the training data are physically accurate (e.g. the results of an experiment or numerical simulation), interpolated quantities can deviate quite far from being physically accurate. Although one could project the output of a network into a physically feasible region, such a postprocess is not captured by the energy function minimized when training the network; thus, the final projected result could incorrectly deviate quite far from the training data. We propose folding any such projection or postprocess directly into the network so that the final result is correctly compared to the training data by the energy function. Although we propose a general approach, we illustrate its efficacy on a specific convolutional neural network that takes in human pose parameters (joint rotations) and outputs a prediction of vertex positions representing a triangulated cloth mesh. While the original network outputs vertex positions with erroneously high stretching and compression energies, the new network trained with our physics prior remedies these issues producing highly improved results.
READ FULL TEXT
Comments
There are no comments yet.